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An Abstraction for Correspondence Search Using Task-Based Controls

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9009))

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Abstract

The correspondence problem (finding matching regions in images) is a fundamental task in computer vision. While the concept is simple, the complexity of feature detectors and descriptors has increased as they provide more efficient and higher quality correspondences. This complexity is a barrier to developers or system designers who wish to use computer vision correspondence techniques within their applications. We have designed a novel abstraction layer which uses a task-based description (covering the conditions of the problem) to allow a user to communicate their requirements for the correspondence search. This is mainly based on the idea of variances which describe how sets of images vary in blur, intensity, angle, etc. Our framework interprets the description and chooses from a set of algorithms those that satisfy the description. Our proof-of-concept implementation demonstrates the link between the description set by the user and the result returned. The abstraction is also at a high enough level to hide implementation and device details, allowing the simple use of hardware acceleration.

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Notes

  1. 1.

    http://www.khronos.org/vision.

  2. 2.

    http://openvidia.sourceforge.net.

  3. 3.

    https://developer.apple.com/technologies/mac/graphics-and-animation.html.

  4. 4.

    http://www.shapelogic.org.

  5. 5.

    http://www.mathworks.com/products/computer-vision.

  6. 6.

    http://gandalf-library.sourceforge.net.

  7. 7.

    http://computer-vision-talks.com/articles/2011-08-19-feature-descriptor-comparison-report/.

  8. 8.

    http://lear.inrialpes.fr/people/mikolajczyk/Database/det_eval.html.

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Acknowledgements

We would like to gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Graphics, Animation and New Media Network of Centres of Excellence (GRAND NCE).

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Authors and Affiliations

  1. Human Communication Technologies Laboratory, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Gregor Miller & Sidney Fels

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  1. Gregor Miller
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  2. Sidney Fels
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Correspondence to Gregor Miller .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Miller, G., Fels, S. (2015). An Abstraction for Correspondence Search Using Task-Based Controls. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_17

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

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